Intelligent cooling and transferring system for anode carbon blocks

Abstract

The invention relates to the technical field of anode carbon blocks, and discloses an intelligent anode carbon block cooling and transferring system which comprises a cooling control module, a transferring management module, an intelligent optimization module, a safety monitoring module, a self-adaptive control module and a report generation module. By making multi-stage dynamic cooling strategy parameters and setting differentiated cooling curves for carbon blocks of different specifications, the cooling treatment accuracy of the carbon blocks of different batches is guaranteed, and meanwhile, the temperature monitoring unit is used for collecting surface and internal temperature distribution data of the carbon blocks in real time; whether local overheating and uneven cooling occur in the cooling process or not can be detected in real time, the stability of the cooling quality of the carbon block is guaranteed, the risk of thermal stress cracks is further reduced, when the posture is detected to be abnormal, the moving track is corrected in real time through a servo motor of the transfer execution unit and a path planning algorithm, and the stability of the carbon block is improved. And the stability and the safety in the carbon block transferring process are ensured.

Description

Technical Field

[0001] This invention relates to the field of anode carbon block technology, specifically to an intelligent cooling and transfer system for anode carbon blocks. Background Technology

[0002] Anode carbon blocks refer to carbon blocks produced using petroleum coke and pitch coke as aggregates and coal tar pitch as binder. They are used as anode materials in prebaked aluminum electrolysis cells. These carbon blocks have been roasted and have a stable geometric shape, so they are also called prebaked anode carbon blocks, or conventionally known as carbon anodes for aluminum electrolysis.

[0003] Currently, due to the various physicochemical changes that occur during the cooling process of anode carbon blocks, the cooling device used during the cooling and transfer of carbon blocks acts on the surface of the carbon blocks and cannot detect in real time whether local overheating or insufficient cooling occurs during the cooling process. When uneven temperature distribution occurs, it can lead to thermal stress cracks and cause a decrease in the quality of the carbon blocks or even scrapping them, making it impossible to guarantee the accuracy of the cooling effect.

[0004] Therefore, an intelligent cooling and transfer system for anode carbon blocks is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent cooling and transfer system for anode carbon blocks, which solves the problems mentioned in the background technology, such as thermal stress cracking, which leads to a decline in the quality of carbon blocks or even scrapping, and the inability to guarantee the accuracy of the cooling effect.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent cooling and transfer system for anode carbon blocks, comprising: The cooling control module uses the temperature monitoring unit to generate charcoal block temperature distribution data, sets dynamic cooling specifications through the cooling strategy configuration unit, and outputs cooling control data through the cooling execution unit. The transfer management module receives the cooling control data, collects the real-time position information of the charcoal blocks through the position tracking unit, executes the charcoal block transfer action through the transfer execution unit, and outputs the transfer status data through the status feedback unit. The intelligent optimization module receives the transfer status data, constructs an efficiency optimization matrix through the data analysis unit, outputs the optimization results through the optimization strategy generation unit, and provides real-time optimization instructions through the real-time adjustment unit. The safety monitoring module receives the optimization results output by the intelligent optimization module, generates monitoring instructions by calling the safety database through the anomaly detection unit and alarm unit, and adjusts the cooling and transfer parameters through the feedback control unit. The adaptive control module receives the optimization results output by the intelligent optimization module, analyzes the system performance characteristics through the performance profile construction unit, identifies system weaknesses through the weakness analysis unit, and generates control commands through the parameter adjustment unit. The report generation module integrates the optimization results with system operation data, and generates a visual performance report through a multi-indicator fusion unit and a weight calculation unit.

[0007] Preferably, the temperature monitoring unit in the cooling control module includes an infrared thermal imager and a distributed temperature sensor array. The infrared thermal imager scans the surface temperature distribution of the carbon block in real time, and the distributed temperature sensor array is embedded in the cooling medium pipeline to monitor the flow temperature of the coolant. The cooling strategy configuration unit dynamically generates multi-level cooling curves based on preset carbon block material parameters and ambient temperature and humidity data.

[0008] Preferably, the location tracking unit in the transfer management module adopts GPS-RFID fusion technology, including RFID tags installed on the carbon blocks and GPS base stations deployed in the factory area, to locate the carbon blocks in real time in three dimensions. The transfer execution unit includes a servo motor-driven robotic arm and an intelligent AGV trolley. The robotic arm is equipped with a force feedback sensor to adaptively grasp the carbon blocks, and the intelligent AGV trolley optimizes the transfer route through a path planning algorithm.

[0009] Preferably, the data analysis unit in the intelligent optimization module uses machine learning algorithms to construct an efficiency optimization matrix, including convolutional neural networks to process temperature distribution data and transport trajectory data to predict cooling efficiency bottlenecks. The optimization strategy generation unit integrates a reinforcement learning model to generate adaptive cooling rate and transport speed optimization schemes based on historical operating data.

[0010] Preferably, the anomaly detection unit in the safety monitoring module includes a vibration sensor and an acoustic emission detector to identify structural cracks and thermal stress anomalies during the cooling process of the carbon block. The alarm unit is connected to the cloud-based safety database and generates graded alarm commands by calling a typical fault case library through a pattern matching algorithm. The feedback control unit uses a PID controller to dynamically adjust the cooling medium flow rate and the acceleration parameters of the transfer robotic arm.

[0011] Preferably, the performance profile building unit in the adaptive control module extracts multi-dimensional indicators such as carbon block cooling rate, transfer energy consumption and system stability based on big data analysis to build a dynamic performance profile. The weak point analysis unit uses a decision tree algorithm to identify shortcomings such as uneven cooling and transfer delay. The parameter control unit generates cooling temperature setpoint and transfer path replanning instructions through a fuzzy logic controller.

[0012] Preferably, the multi-index fusion unit in the report generation module integrates cooling efficiency, transfer time, energy consumption ratio and safety event rate indicators, calculates weights using the entropy weight method, and the weight calculation unit dynamically allocates index weights based on the AHP (Analytic Hierarchy Process). The visualized performance report is output in the form of a three-dimensional heat map and line graph, and is displayed in real time through the HMI (Human-Machine Interface).

[0013] Preferably, the data analysis unit in the intelligent optimization module integrates a 5G communication unit and an edge computing node. The 5G communication unit synchronizes the temperature distribution data of the cooling control module and the location information of the transfer management module to the cloud database in real time. The edge computing node deploys a convolutional neural network model to process the temperature monitoring data and transfer trajectory data locally in a distributed manner, generating a low-latency efficiency optimization matrix. The multi-indicator fusion unit of the report generation module calls historical data from the cloud through the 5G communication unit and dynamically updates the weight allocation strategy in combination with the entropy weight method.

[0014] Preferably, the cooling execution unit includes a variable frequency cooling pump and an intelligent valve array. The variable frequency cooling pump adjusts the coolant flow rate through a PID algorithm, and the intelligent valve array dynamically allocates cooling areas based on temperature monitoring unit data. The status feedback unit in the transfer execution unit integrates a pressure sensor and an inertial measurement unit to provide real-time feedback on carbon block gripping force and transfer vibration data.

[0015] Preferably, the feedback control unit of the safety monitoring module integrates redundant control logic. When the anomaly detection unit detects abnormal cooling medium flow and excessive transport vibration, it automatically switches to the backup control strategy. The intelligent valve array of the cooling actuator isolates the faulty cooling area and increases the coolant flow rate in adjacent areas; The servo motor deceleration protocol of the transfer execution unit is invoked, and a risk avoidance route is generated in combination with the path planning algorithm; The parameter adjustment unit of the adaptive control module receives redundant control commands in real time and adjusts the cooling temperature setpoint and the upper limit of the transfer acceleration through a fuzzy logic controller. Beneficial effects

[0016] Compared with the prior art, the present invention provides an intelligent cooling and transfer system for anode carbon blocks, which has the following beneficial effects: 1. In this invention, during the cooling and transfer of anode carbon blocks, multi-level dynamic cooling strategy parameters are formulated, and differentiated cooling curves are set for carbon blocks of different specifications to ensure the accuracy of cooling treatment for different batches of carbon blocks. At the same time, the temperature monitoring unit collects the temperature distribution data of the surface and interior of the carbon blocks in real time, which can detect whether local overheating and uneven cooling occur during the cooling process in real time, ensuring the stability of the cooling quality of the carbon blocks and further reducing the risk of thermal stress cracking.

[0017] 2. In this invention, during the transfer of anode carbon blocks, the offset between the real-time position of the carbon block and the preset path is calculated by the position tracking unit, and the transfer vibration intensity is monitored by the inertial measurement unit. The system can determine in real time whether the carbon block has positional deviation and mechanical impact, so that the system can reduce the probability of surface damage to the carbon block during the transfer process. When an abnormal posture is detected, the servo motor of the transfer execution unit and the path planning algorithm correct the running trajectory in real time, ensuring the stability and safety of the carbon block transfer process.

[0018] 3. In this invention, during the cooling and transfer of anode carbon blocks, a multi-condition adaptive control model is constructed through an intelligent optimization module. Based on the carbon block specifications, environmental parameters, and real-time operating data, the cooling intensity and transfer speed are dynamically adjusted to achieve precise adaptation under different production batches and process requirements. This enables the system to improve cooling efficiency and transfer coordination, reduce energy consumption and quality fluctuations caused by mismatched process parameters, and further improve the overall reliability and economy of anode carbon block production. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the intelligent cooling and transfer system for anode carbon blocks according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 The intelligent cooling and transfer system for anode carbon blocks includes: The cooling control module uses the temperature monitoring unit to generate charcoal block temperature distribution data, sets dynamic cooling specifications through the cooling strategy configuration unit, and outputs cooling control data through the cooling execution unit. The transfer management module receives cooling control data, collects real-time location information of charcoal blocks through the position tracking unit, executes charcoal block transfer actions through the transfer execution unit, and outputs transfer status data through the status feedback unit. The intelligent optimization module receives transit status data, constructs an efficiency optimization matrix through the data analysis unit, outputs optimization results through the optimization strategy generation unit, and provides real-time optimization instructions through the real-time adjustment unit. The safety monitoring module receives the optimization results output by the intelligent optimization module, generates monitoring instructions by calling the safety database through the anomaly detection unit and alarm unit, and adjusts the cooling and transfer parameters through the feedback control unit. The adaptive control module receives the optimization results output by the intelligent optimization module, analyzes the system performance characteristics through the performance profiling unit, identifies system weaknesses through the weakness analysis unit, and generates control commands through the parameter adjustment unit. The report generation module integrates optimization results with system operation data, and generates a visual performance report through a multi-indicator fusion unit and a weight calculation unit. The temperature monitoring unit in the cooling control module includes an infrared thermal imager and a distributed temperature sensor array. The infrared thermal imager scans the surface temperature distribution of the carbon block in real time, and the distributed temperature sensor array is embedded in the cooling medium pipeline to monitor the flow temperature of the coolant. The cooling strategy configuration unit dynamically generates multi-level cooling curves based on preset carbon block material parameters and ambient temperature and humidity data. Temperature monitoring unit: An infrared thermal imager scans the surface temperature distribution of the carbon block at a sampling frequency of 10Hz with an accuracy of ±0.5℃. A distributed temperature sensor array is embedded in the cooling pipes to monitor the coolant temperature. The sensor spacing is less than 0.1m. Heat conduction model formula:

[0022] in, For heat flow, heat transfer coefficient The surface area of ​​the carbon block. The surface temperature of the carbon block. This refers to the coolant temperature. Cooling strategy configuration unit: Based on the carbon block material parameters and ambient temperature and humidity, multi-level cooling curves are dynamically generated, and the cooling rate is... calculate:

[0023] in, For the target temperature, Cooling time is calculated based on real-time data. Adjustment To ensure temperature gradient K / m; The location tracking unit in the transfer management module adopts GPS-RFID fusion technology, including RFID tags installed on the carbon blocks and GPS base stations deployed in the factory area, to locate the carbon blocks in real time in three dimensions. The transfer execution unit includes a servo motor driven robotic arm and an intelligent AGV trolley. The robotic arm is equipped with a force feedback sensor to adaptively grasp the carbon blocks, and the intelligent AGV trolley optimizes the transfer route through a path planning algorithm. Position tracking unit: GPS-RFID fusion technology enables three-dimensional positioning with an accuracy of ±0.01m and high RFID tag signal strength. GPS base station coordinates charcoal block location calculate:

[0024] in, To account for distance deviation, the system updates location data in real time, with a sampling frequency of 5Hz; Transfer execution unit: Servo motor driven robotic arm gripping force Controlled by a force feedback sensor:

[0025] in, This is the stiffness coefficient. The location offset is used for path planning of the intelligent AGV, which employs the A* algorithm, with the optimization objective being the minimum transfer time.

[0026]

[0027] The data analysis unit in the intelligent optimization module uses machine learning algorithms to construct an efficiency optimization matrix, including convolutional neural networks to process temperature distribution data and transport trajectory data to predict cooling efficiency bottlenecks. The optimization strategy generation unit integrates reinforcement learning models to generate adaptive cooling rate and transport speed optimization schemes based on historical operating data. Data Analysis Unit: A convolutional neural network processes temperature distribution and transport trajectory data. The input layer dimension is 128×128×3, the convolutional kernel size is 3×3, and the output is cooling efficiency. :

[0028] in, For heat flow derivatives, the 5G communication unit synchronizes data to the cloud with a latency of less than 10ms, and the edge computing node runs the convolutional neural network locally to reduce processing latency; Optimization Strategy Generation Unit: Generates optimization schemes for reinforcement learning models. Value update formula:

[0029] in, For state, For action, As a reward, For learning rate, Discount factor; The anomaly detection unit in the safety monitoring module includes vibration sensors and acoustic emission detectors to identify structural cracks and thermal stress anomalies during the cooling process of the carbon block. The alarm unit is connected to the cloud-based safety database and generates graded alarm commands by calling the typical fault case library through a pattern matching algorithm. The feedback control unit uses a PID controller to dynamically adjust the flow rate of the cooling medium and the acceleration parameters of the transfer robotic arm. Anomaly detection unit: Vibration sensors monitor the vibration spectrum, acoustic emission detectors identify crack signals, and thermal stress anomaly thresholds are set.

[0030] in, For thermal stress, The elastic modulus of the carbon block. The coefficient of thermal expansion is For temperature difference, when An alarm is triggered at any time; Alarm unit: The pattern matching algorithm calls the cloud security database to generate tiered alarms; Feedback control unit: PID controller adjusts parameters; redundant control logic automatically switches in case of anomalies: Cooling medium failure: isolates the faulty area and increases the flow rate of adjacent areas. ( ); Excessive vibration during transport: Servo motor speed reduced to... Path replanning to avoid risks; The performance profile building unit in the adaptive control module extracts multi-dimensional indicators such as carbon block cooling rate, transfer energy consumption and system stability based on big data analysis, and builds a dynamic performance profile. The weak point analysis unit uses decision tree algorithm to identify shortcomings such as uneven cooling and transfer delay. The parameter control unit generates cooling temperature setpoint and transfer path replanning instructions through fuzzy logic controller. Performance profiling unit: Big data analysis to extract multi-dimensional indicators: Cooling efficiency Energy consumption during transportation System stability Dynamic portrait formula:

[0031] Weight Dynamically allocated using the AHP algorithm; Weakness Analysis Unit: Decision tree algorithm identifies bottlenecks, uneven cooling, and transport delays. ; Parameter control unit: Adjustment of the fuzzy logic controller output setpoint and cooling temperature. and transport acceleration The rule base is based on expert experience; The multi-index fusion unit in the report generation module integrates indicators such as cooling efficiency, transfer time, energy consumption ratio and safety event rate, and uses the entropy weight method to calculate the weights. The weight calculation unit dynamically allocates index weights based on the AHP analytic hierarchy process. The visual performance report is output in the form of a three-dimensional heat map and line chart, and is displayed in real time through the HMI human-machine interface. Multi-indicator fusion unit: Entropy weight method for calculating weights

[0032]

[0033] in, For the standardized value of the indicator, For the indicator numbers, 5G communication calls historical data from the cloud to update the weights; Weight calculation unit: AHP (Analytical Hierarchy Process) assigns weights to indicators and calculates consistency ratios. ; Visual output: The HMI interface displays 3D heat maps and line graphs with a refresh rate of 30Hz. The data analysis unit in the intelligent optimization module integrates a 5G communication unit and an edge computing node. The 5G communication unit synchronizes the temperature distribution data of the cooling control module and the location information of the transfer management module to the cloud database in real time. The edge computing node deploys a convolutional neural network model to process temperature monitoring data and transfer trajectory data locally in a distributed manner, generating a low-latency efficiency optimization matrix. The multi-indicator fusion unit of the report generation module calls historical data from the cloud through the 5G communication unit and dynamically updates the weight allocation strategy in combination with the entropy weight method. The cooling execution unit includes a variable frequency cooling pump and an intelligent valve array. The variable frequency cooling pump adjusts the coolant flow rate through a PID algorithm, and the intelligent valve array dynamically allocates the cooling area based on the temperature monitoring unit data. The status feedback unit in the transfer execution unit integrates a pressure sensor and an inertial measurement unit to provide real-time feedback on the carbon block gripping force and transfer vibration data. Cooling actuator: The variable frequency cooling pump adjusts the coolant flow rate using a PID algorithm. :

[0034] in, For temperature deviation, , , The PID gain coefficient is used to dynamically allocate cooling zones and valve opening based on temperature distribution data in the intelligent valve array. and Negative correlation; Status feedback unit: Pressure sensors and inertial measurement units monitor the gripping force in real time. and vibration acceleration The data is fed back to the intelligent optimization module; The feedback control unit of the safety monitoring module integrates redundant control logic. When the anomaly detection unit detects abnormal cooling medium flow and excessive transport vibration, it automatically switches to the backup control strategy. The intelligent valve array of the cooling actuator isolates the faulty cooling area and increases the coolant flow rate in adjacent areas; The servo motor deceleration protocol of the transfer execution unit is invoked, and a risk avoidance route is generated in combination with the path planning algorithm; The parameter adjustment unit of the adaptive control module receives redundant control commands in real time and adjusts the cooling temperature setpoint and the upper limit of the transfer acceleration through the fuzzy logic controller.

[0035] The operation steps of the intelligent cooling and transfer system for anode carbon blocks are as follows: 1. System initialization and parameter configuration Principle Description: When the system starts, it generates dynamic cooling specifications through the cooling strategy configuration unit of the cooling control module based on the preset charcoal block specifications and environmental parameters. This includes formulating multi-level cooling curves based on charcoal block material parameters and environmental data to ensure differentiated treatment for different batches of charcoal blocks during the cooling process. At the same time, the transfer management module initializes the path planning algorithm and presets the charcoal block transfer route.

[0036] Technical support: The cooling strategy configuration unit dynamically adjusts cooling parameters using a preset database and real-time environmental sensor data. The intelligent optimization module preloads historical operating data in the background to prepare for subsequent optimization. This step ensures that the system achieves accurate adaptation from the beginning, avoiding increased energy consumption and quality fluctuations caused by mismatched process parameters.

[0037] 2. Real-time temperature monitoring and cooling execution Principle Description: After the carbon block enters the cooling zone, the temperature monitoring unit scans the surface temperature distribution of the carbon block in real time and monitors the flow temperature of the coolant. The infrared thermal imager captures the surface thermal map, and distributed sensors are embedded in the cooling pipes to collect internal temperature data. When the system detects local overheating and uneven cooling, the cooling execution unit dynamically adjusts the coolant flow rate through a PID algorithm and allocates the flow rate to the cooling zone to ensure that the carbon block temperature drops uniformly and reduce the risk of thermal stress cracking.

[0038] Technical support: Temperature data is synchronized to the cloud database in real time via a 5G communication unit. Based on the temperature monitoring data, the cooling execution unit uses a PID controller to precisely control the cooling intensity. This step realizes real-time closed-loop control of charcoal block cooling and ensures the stability of cooling quality.

[0039] 3. Carbon block transfer and trajectory control Principle Description: After cooling is complete, the transfer management module takes over the operation. The position tracking unit uses GPS-RFID fusion technology to locate the carbon block in real time in three dimensions and calculate the offset from the preset path. The transfer execution unit performs grasping and transfer. The robotic arm adaptively grasps the carbon block through force feedback sensors. The AGV trolley optimizes the transfer route with path planning algorithms. When the status feedback unit detects an abnormal posture, the system corrects the trajectory in real time through servo motors and path planning algorithms to ensure smooth transfer.

[0040] Technical support: The transfer process utilizes machine learning algorithms to process trajectory data, and a safety monitoring module intervenes to detect anomalies. When vibration exceeds the limit, a deceleration protocol and an avoidance route are triggered, which reduces the probability of surface damage to the carbon block.

[0041] 4. Intelligent optimization and dynamic parameter adjustment Principle Description: During system operation, the intelligent optimization module receives transfer status data, constructs an efficiency optimization matrix through the data analysis unit, processes temperature distribution and transfer trajectory data through a convolutional neural network to predict cooling efficiency bottlenecks, and integrates a reinforcement learning model into the optimization strategy generation unit to output adaptive solutions based on historical data. The optimization results are fed back to the cooling and transfer modules in real time to ensure that the system maintains high efficiency and collaboration under different production batches.

[0042] Technical support: Edge computing nodes process data locally to reduce latency; the adaptive control module identifies shortcomings based on performance profiles and generates replanning instructions through a fuzzy logic controller, further improving the overall reliability and economy of the system.

[0043] 5. Security monitoring and anomaly response Principle Description: The safety monitoring module monitors the operation status throughout the process, the anomaly detection unit identifies potential risks, and the alarm unit connects to the cloud-based safety database. It uses a pattern matching algorithm to call the fault case library and generate tiered alarms. When abnormal cooling medium flow and excessive transfer vibration are detected, the feedback control unit activates redundant control logic, isolates the faulty cooling area and increases the flow of adjacent areas, calls the servo motor deceleration protocol to generate an avoidance route, and at the same time, the adaptive control module adjusts the parameters in real time.

[0044] Technical support: The PID controller dynamically adjusts the parameters, and the fuzzy logic controller ensures a smooth response. This step minimizes the risk of quality defects.

[0045] 6. Performance Report Generation and System Feedback Principle Description: Upon completion of the operation, the report generation module integrates the optimization results and system operation data. The multi-indicator fusion unit calculates the weights of key indicators and dynamically allocates the weights using the entropy weight method and AHP (Analytic Hierarchy Process). Finally, a visual performance report is generated and displayed in real-time through the HMI (Human-Machine Interface). This report serves as a production review, guiding subsequent strategy optimization.

[0046] Technical support: The 5G communication unit calls on historical data in the cloud to update the weight strategy. The report is generated based on the fusion of multiple indicators to ensure data-driven decision-making. This step provides closed-loop feedback and supports continuous improvement.

[0047] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0048] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent cooling and transfer system for anode carbon blocks, characterized in that: include: The cooling control module uses the temperature monitoring unit to generate charcoal block temperature distribution data, sets dynamic cooling specifications through the cooling strategy configuration unit, and outputs cooling control data through the cooling execution unit. The transfer management module receives the cooling control data, collects the real-time position information of the charcoal blocks through the position tracking unit, executes the charcoal block transfer action through the transfer execution unit, and outputs the transfer status data through the status feedback unit. The intelligent optimization module receives the transfer status data, constructs an efficiency optimization matrix through the data analysis unit, outputs the optimization results through the optimization strategy generation unit, and provides real-time optimization instructions through the real-time adjustment unit. The safety monitoring module receives the optimization results output by the intelligent optimization module, generates monitoring instructions by calling the safety database through the anomaly detection unit and alarm unit, and adjusts the cooling and transfer parameters through the feedback control unit. The adaptive control module receives the optimization results output by the intelligent optimization module, analyzes the system performance characteristics through the performance profile construction unit, identifies system weaknesses through the weakness analysis unit, and generates control commands through the parameter adjustment unit. The report generation module integrates the optimization results with system operation data, and generates a visual performance report through a multi-indicator fusion unit and a weight calculation unit.

2. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that: The temperature monitoring unit in the cooling control module includes an infrared thermal imager and a distributed temperature sensor array. The infrared thermal imager scans the surface temperature distribution of the carbon block in real time, and the distributed temperature sensor array is embedded in the cooling medium pipeline to monitor the flow temperature of the coolant. The cooling strategy configuration unit dynamically generates multi-level cooling curves based on preset carbon block material parameters and ambient temperature and humidity data.

3. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that: The location tracking unit in the transfer management module adopts GPS-RFID fusion technology, including RFID tags installed on the carbon blocks and GPS base stations deployed in the factory area, to locate the carbon blocks in real time in three dimensions. The transfer execution unit includes a servo motor driven robotic arm and an intelligent AGV trolley. The robotic arm is equipped with a force feedback sensor to adaptively grasp the carbon blocks, and the intelligent AGV trolley optimizes the transfer route through a path planning algorithm.

4. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that: The data analysis unit in the intelligent optimization module uses machine learning algorithms to construct an efficiency optimization matrix, including convolutional neural networks to process temperature distribution data and transport trajectory data to predict cooling efficiency bottlenecks. The optimization strategy generation unit integrates a reinforcement learning model to generate adaptive cooling rate and transport speed optimization schemes based on historical operating data.

5. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that: The anomaly detection unit in the safety monitoring module includes a vibration sensor and an acoustic emission detector to identify structural cracks and thermal stress anomalies during the cooling process of the carbon block. The alarm unit is connected to the cloud-based safety database and generates graded alarm commands by calling a typical fault case library through a pattern matching algorithm. The feedback control unit uses a PID controller to dynamically adjust the cooling medium flow rate and the acceleration parameters of the transfer robotic arm.

6. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that: The performance profile building unit in the adaptive control module extracts multi-dimensional indicators such as carbon block cooling rate, transfer energy consumption and system stability based on big data analysis to build a dynamic performance profile. The weak point analysis unit uses a decision tree algorithm to identify shortcomings such as uneven cooling and transfer delay. The parameter control unit generates cooling temperature setpoint and transfer path replanning instructions through a fuzzy logic controller.

7. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that: The multi-index fusion unit in the report generation module integrates indicators such as cooling efficiency, transfer time, energy consumption ratio, and safety event rate, and calculates the weights using the entropy weight method. The weight calculation unit dynamically allocates index weights based on the AHP (Analytic Hierarchy Process) method. The visualized performance report is output in the form of a three-dimensional heat map and line graph, and is displayed in real time through the HMI (Human-Machine Interface).

8. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that: The data analysis unit in the intelligent optimization module integrates a 5G communication unit and an edge computing node. The 5G communication unit synchronizes the temperature distribution data of the cooling control module and the location information of the transfer management module to the cloud database in real time. The edge computing node deploys a convolutional neural network model to process temperature monitoring data and transfer trajectory data locally in a distributed manner, generating a low-latency efficiency optimization matrix. The multi-indicator fusion unit of the report generation module calls historical data from the cloud through the 5G communication unit and dynamically updates the weight allocation strategy in combination with the entropy weight method.

9. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that: The cooling execution unit includes a variable frequency cooling pump and an intelligent valve array. The variable frequency cooling pump adjusts the coolant flow rate through a PID algorithm, and the intelligent valve array dynamically allocates cooling areas based on data from the temperature monitoring unit. The status feedback unit in the transfer execution unit integrates a pressure sensor and an inertial measurement unit to provide real-time feedback on the carbon block gripping force and transfer vibration data.

10. The intelligent cooling and transfer system for anode carbon blocks according to claim 1, characterized in that, The feedback control unit of the safety monitoring module integrates redundant control logic. When the anomaly detection unit detects abnormal cooling medium flow and excessive transport vibration, it automatically switches to the backup control strategy. The intelligent valve array of the cooling actuator isolates the faulty cooling area and increases the coolant flow rate in adjacent areas; The servo motor deceleration protocol of the transfer execution unit is invoked, and a risk avoidance route is generated in combination with the path planning algorithm; The parameter adjustment unit of the adaptive control module receives redundant control commands in real time and adjusts the cooling temperature setpoint and the upper limit of the transfer acceleration through a fuzzy logic controller.

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